We downscale Santa Ana winds (SAWs) from eight global climate models (GCMs) and validate key aspects of their climatology over the historical period. We then assess SAW evolution and behavior through the 21st century, paying special attention to changes in their extreme occurrences. All GCMs project decreases in SAW activity, starting in the early 21st century, which are commensurate with decreases in the southwestward pressure gradient force that drives these winds. The trend is most pronounced in the early and late SAW season: fall and spring. It is mainly determined by changes in the frequency of SAW events, less so by changes in their intensity. The peak of the SAW season (November-December-January) is least affected by anthropogenic climate change in GCM projections. Plain Language Summary Dry and gusty Santa Ana winds (SAWs) drive the most catastrophic wildfires in Southern California. Their sensitivity to the changing climate has been a matter of uncertainty and debate. We have assessed the response of SAW activity to global warming and describe these results in detail here. The overall decrease in SAW activity robustly projected by downscaled global climate models is strongest in the early and late seasons-fall and spring. SAWs are expected to decrease least at the peak of their season approximately December. Importantly, decreased SAW activity in the future climate is driven mainly by decreased frequency rather than the peak intensity of these winds. These results, together with what we know from recent literature about how precipitation is projected to change in this region, suggest a later wildfire season in the future.

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month "proof-of-concept" trial run designed to illustrate the utility and feasibility of the ARTMIP project.

We examine mechanisms driving daily variability of summer coastal low cloudiness (CLC) along the California coast. Daily CLC is derived from a satellite record from 1996 to 2014. Atmospheric rather than oceanic processes are mostly responsible for daily fluctuations in vertical stability that dictate short-period variation in CLC structure. Daily CLC anomalies are most strongly correlated to lower tropospheric stability anomalies to the north. The spatially offset nature of the cloud-stability relationship is a result of the balancing act that affects low cloudiness wherein subsidence drives increased stability, which promotes cloudiness, but too much subsidence limits cloudiness. Lay explanations claim that high inland temperatures pull in CLC, but such a process presumably would have the high temperatures directly inland. Rather, we find that the spatially offset associations between CLC and atmospheric circulation result in positive correlations between CLC and inland surface temperature anomalies to the north.

Santa Ana Winds (SAWs) are an integral feature of the regional climate of Southern California/Northern Baja California region, but their climate-scale behavior is poorly understood. In the present work, we identify SAWs in mesoscale dynamical downscaling of a global reanalysis from 1948 to 2012. Model winds are validated with anemometer observations. SAWs exhibit an organized pattern with strongest easterly winds on westward facing downwind slopes and muted magnitudes at sea and over desert lowlands. We construct hourly local and regional SAW indices and analyze elements of their behavior on daily, annual, and multidecadal timescales. SAWs occurrences peak in winter, but some of the strongest winds have occurred in fall. Finally, we observe that SAW intensity is influenced by prominent large-scale low-frequency modes of climate variability rooted in the tropical and north Pacific ocean-atmosphere system.